CISUC - On the benefits of multidimensional scaling in epileptic seizure prediction
CISUC

On the benefits of multidimensional scaling in epileptic seizure prediction

Authors

Abstract

Algorithms for Epileptic seizure prediction using var- ious features extracted from the multichannel Electroencephalo- graphic (EEG) signals, need to work in high dimensional spaces, leading to increased difficulties in computational time and convergence conditions. Multidimensional Scaling (MDS) is a technique to surpass this curse of dimensionality in classification problems. In this work we investigate the influence of dimensional reduction in classification performance by previously applying Multidimensional Scaling and then applying Support Vector Machines (SVM) to classify the brain state. Data from five patients of the European Database on Epilepsy of the FP7 EPILEPSIAE Project is used. The results show that dimension reduction improves less than expected the SVM performance.

Keywords

epileptic seizure prediction; multidimensional scaling, Support vector machines

Subject

epileptic seizure prediction

Conference

1st Portugese Meeting in Bioengineering, March 2011


Cited by

Year 2014 : 1 citations

 Ghaderyan, Peyvand, Ataollah Abbasi, and Mohammad Hossein Sedaaghi. "An efficient seizure prediction method using KNN-based undersampling and linear frequency measures." Journal of neuroscience methods 232 (2014): 134-142.